Efficacy of Educational Interventions in Improving Measures of Living-donor Kidney Transplantation Activity: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: To address patient-level barriers to living-donor kidney transplantation (LDKT), centers have implemented educational interventions. Recently, some have highlighted several gaps in knowledge and lack of evidence of efficacy of these interventions. No review has synthesized the available data. METHODS: We conducted a systematic review and meta-analysis of studies conducted to increase measures of LDKT. Outcomes of interest were LDKT rates, donor evaluation, donor contact/inquiry, total transplantation rates, and change in knowledge scores and pursuit behaviors. A literature search was conducted across 7 databases from inception until 2017. Educational interventions were a decision/teaching aid alone or with personalized sessions. Comparator was another intervention or nonspecific education. Random effects meta-analysis was performed to pool risk ratios (RRs) across studies. RESULTS: Of the 1813 references, 15 met the inclusion criteria; 9 were randomized control trials. When compared with nonspecific education, interventions increased LDKT rates (RR = 2.54; 95% confidence interval [CI], 1.49-4.35), donor evaluation (RR = 3.82; 95% CI, 1.91-7.64), and donor inquiry/contact (RR = 2.41; 95% CI, 1.53-3.80), but not total transplants (RR = 1.24; 95% CI, 0.96-1.61). Significant increased mean knowledge scores postintervention was noted, and most showed favorable trends in pursuit behaviors. Quality across the studies was mixed and sometimes difficult to assess. The biggest limitations were small sample size, selection bias, and short follow-ups. CONCLUSIONS: Educational interventions improve measures of LDKT activity; however, current literature is heterogeneous and at risk of selection bias. Prospective studies with diverse patient populations, longer follow-ups, and robust outcomes are needed to inform clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".